A Complex mHealth Coaching Intervention to Prevent Overweight, Obesity, and Diabetes in High-Risk Women in Antenatal Care: Protocol for a Hybrid Type 2 Effectiveness-Implementation Study
Notice bibliographique
Résumé
BACKGROUND: Women with overweight and obesity are at higher risk of developing complications in pregnancy such as gestational diabetes and longer-term chronic conditions. Research concerning health behavior change interventions during pregnancy and postpartum shows promising effects, but implementation into routine services is sparsely investigated. Most interventions focus on the antenatal or postpartum life stages, failing to meet the needs of women. IMPACT DIABETES Bump2Baby is a multicenter project across 4 high-income countries developed to test the implementation of an antenatal and postpartum evidence-based mobile health (mHealth) coaching intervention called Bump2Baby and Me (B2B&Me) designed to sit alongside usual care in the perinatal period. OBJECTIVE: We aim to explore the feasibility and implementation of the B2B&Me intervention and investigate the effectiveness of this intervention in women at risk of gestational diabetes. METHODS: IMPACT DIABETES Bump2Baby is a hybrid type 2 effectiveness-implementation study, which integrates an evidence-based mHealth coaching app that includes personalized health behavior change coaching provided by health care professionals alongside antenatal care from the first antenatal visit to 12 months postpartum. The mHealth app offers the possibility of synchronous calls, asynchronous contact (including coach-participant text and video messaging exchanges tailored to the participant's needs), and ongoing access to an extensive library of bespoke intervention materials. Participants will interact asynchronously with their health coach throughout the intervention via the app. This randomized controlled trial across 4 clinical sites within Ireland, the United Kingdom, Spain, and Australia will recruit 800 women in early pregnancy to evaluate the effectiveness on postpartum weight. The Exploration, Preparation, Implementation, and Sustainment implementation framework is the theoretical underpinning of the study. The implementation evaluation will be assessed at the individual, hospital staff, and broader community levels using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework. Data sources for the RE-AIM evaluation will include app and platform analytics, screening and training records, participant medical records, key informant interviews, participant and partner exit interviews, cost data, study questionnaires, staff surveys, and blood sample analyses. RESULTS: The study was approved and registered with the Australian New Zealand Clinical Trials Registry on November 19, 2020. Recruitment commenced on February 9, 2021, and data collection is ongoing. Publication of the results is expected in 2024. CONCLUSIONS: This is the first hybrid effectiveness-implementation study of an 18-month mHealth coaching intervention in at-risk women that we are aware of. As research aims to move toward real-world implementable solutions, it is critical that hybrid studies are conducted. The data from this large multicenter study will be useful in planning the potential implementation and scale-up of evidence-based perinatal health behavior change interventions. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12620001240932; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380020&isReview=true. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/51431.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,003 |
| Méta-épidémiologie (sens large) | 0,007 | 0,006 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,061 | 0,008 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».